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Conceptual Understanding through Efficient Automated Design of Quantum Optical Experiments

机译：通过高效自动化设计的概念理解量子光学实验

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摘要

Artificial intelligence (AI) is a potentially disruptive tool for physics and science in general. One crucial question is how this technology can contribute at a conceptual level to help acquire new scientific understanding. Scientists have used AI techniques to rediscover previously known concepts. So far, no examples of that kind have been reported that are applied to open problems for getting new scientific concepts and ideas. Here, we present T heseus , an algorithm that can provide new conceptual understanding, and we demonstrate its applications in the field of experimental quantum optics. To do so, we make four crucial contributions. (i)?We introduce a graph-based representation of quantum optical experiments that can be interpreted and used algorithmically. (ii)?We develop an automated design approach for new quantum experiments, which is orders of magnitude faster than the best previous algorithms at concrete design tasks for experimental configuration. (iii)?We solve several crucial open questions in experimental quantum optics which involve practical blueprints of resource states in photonic quantum technology and quantum states and transformations that allow for new foundational quantum experiments. Finally, and most importantly, (iv)?the interpretable representation and enormous speed-up allow us to produce solutions that a human scientist can interpret and gain new scientific concepts from outright. We anticipate that T heseus will become an essential tool in quantum optics for developing new experiments and photonic hardware. It can further be generalized to answer open questions and provide new concepts in a large number of other quantum physical questions beyond quantum optical experiments. T heseus is a demonstration of explainable AI (XAI) in physics that shows how AI algorithms can contribute to science on a conceptual level.
机译：人工智能（AI）是一般的物理和科学的潜在破坏性工具。一个至关重要的问题是这项技术如何在概念层面贡献，以帮助获得新的科学理解。科学家使用AI技术来重新发现以前已知的概念。到目前为止，没有报告这种情况的例子，适用于开辟新的科学概念和想法的问题。在这里，我们展示了一种可以提供新的概念理解的算法，并且我们展示了其在实验量子光学领域的应用。为此，我们做出了四个至关重要的贡献。 （i）？我们介绍了可以解释和使用的量子光学实验的基于图的基于图形的表示。 （ii） （iii）最后，最重要的是（IV）？可解释的代表和巨大的加速使我们能够产生人类科学家可以从彻底解释和获得新的科学概念的解决方案。我们预计T HESHUS将成为量子光学元件的重要工具，用于开发新的实验和光子硬件。它可以进一步推广以回答开放性问题，并在超出量子光学实验的大量其他量子物理问题中提供新的概念。 T Heshus是可解释的AI（Xai）的示范，其展示了AI算法如何在概念层面上有助于科学。

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